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We talk about “The City” in London like we do about “The Wall Street” in New York when we mean the financial districts of each place. But it’s a bit more complicated than that in London.

Yes, the City is the old and traditional centre of banking and insurance in the UK. And it’s still a major location for financial firms (interestingly, even within “the square mile” there are clear subdivisions: banks on this side, insurers on that side). Canary Wharf in the old docklands, since the 1980’s, has lots of banks too, especially American investment banks.

But if you want to find hedge funds and other boutique outfits, you best head west: Mayfair and other posh bits of the West End is where they have been congregating since the noughties or so. And finally, there’s the “Silicon Roundabout”, just north of the city, where the latest breed of “fintech” start-ups live.

So – all in all we have four separate financial. districts in one city. It could be considered just a bit excessive, couldn’t it?

Statistical hypothesis testing has always been close to my heart. I’ve long been critical of the use of p values, especially as most people seem to misunderstand their interpretation. I may even have failed a job interview once due to my stance on this. I suspect my interviewers didn’t believe that I knew what I was talking about when the subject came up.

This week, I read another two papers on this theme, one in finance, one in psychology. The first, Evaluating Trading Strategies by Campbell Harvey and Yan Liu looks at empirical evaluation of stock trading strategies. It is a nice illustration of the usual pitfalls of data mining – the bad kind where you do so many tests that some are bound to be statistically significant by chance alone – and has a useful discussion of ways of correcting for such multiple testing.

These two papers aren’t anything revolutionarily new, as this stuff has been known and talked about for ages. And yet, lots of people still fall into these traps, especially when it comes to publishing their work, and evaluating previous findings (for example doing a meta-analysis). My first thought was that it must be due to institutional factors: academics must publish, so they will fish and mine and p hack until they find something statistically significant to publish, and journals need to fill their pages somehow so they stick to the old rules, even though everyone knows that it’s all a bit of a sham and not to be trusted.

But then I came across Hoekstra, Morey, Rouder & Wagenmakers’s Robust misinterpretation of confidence intervals. This one interviewed 120 psychology researchers and 442 students about their understanding of confidence intervals for a simple hypothesis test. Both groups were equally misinformed about the interpretation of the test results, such as confidence intervals and p values. And what’s more (quoting from the abstract): “Self-declared experience with statistics was not related to researchers’ performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever.”